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MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia

MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Armstrong et al, Nature Genetics 30, 41-47 (2002). Blank slide/colon data. Hsa.37937 3' UTR 2a 197371 MYOSIN HEAVY CHAIN, NONMUSCLE (Gallus gallus). gene1. tumor:.

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MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia

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  1. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia Armstrong et al, Nature Genetics 30, 41-47 (2002)

  2. Blank slide/colon data

  3. Hsa.37937 3' UTR 2a 197371 MYOSIN HEAVY CHAIN, NONMUSCLE (Gallus gallus) gene1 tumor: 1.62 1.33 0.79 0.41 0.39 0.38 1.22 1.57 0.72 0.97 1.12 0.61 0.79 0.36 0.52 0.58 0.44 0.35 0.53 0.52 0.46 0.59 0.68 0.27 0.67 0.49 0.49 0.53 0.35 1.44 0.55 0.33 1.70 0.59 0.73 1.54 1.03 0.54 0.66 0.33 mean = 0.73 std = 0.4 normal: 2.81 2.18 2.68 2.17 2.84 2.58 4.97 2.12 2.76 3.41 2.72 3.26 2.51 1.24 2.83 1.25 4.22 1.06 2.30 0.44 1.21 1.57 mean = 2.41 std = 1.05

  4. histograms 2.812.182.682.172.842.584.972.122.76 3.41 2.72 3.26 2.511.242.831.254.221.062.300.441.211.57 HISTOGRAM, BINS OF 0.5 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5 1 3 2 3 5 4 2 1 1

  5. NORMALIZED (FREQUENCIES) mean = 0.73 std = 0.4 mean = 2.41 std = 1.05

  6. T = -9.04 P = 10 e-14 t-test

  7. Hsa.37192 3' UTR 2a 186603 EUKARYOTIC INITIATION FACTOR 4B (Homo sapiens) gene1000 tumor: 0.21 0.38 0.51 0.23 0.23 0.32 0.20 0.53 0.33 0.47 0.25 0.22 0.36 0.26 0.27 0.26 0.26 0.33 0.30 0.15 0.25 0.18 0.19 0.28 0.25 0.25 0.54 0.20 0.41 0.47 0.49 0.39 0.33 0.44 0.37 0.42 0.34 0.35 0.56 0.37 mean = 0.328 std = 0.111 normal: 0.20 0.32 0.62 0.21 0.31 0.25 0.24 0.40 0.25 0.50 0.19 0.37 0.63 0.33 0.41 0.48 0.59 0.45 0.48 0.31 0.30 0.41 mean = 0.375 std = 0.134

  8. histograms

  9. NORMALIZED (FREQUENCIES)

  10. 85% T = -1.48 P = 0.15 t-test

  11. Hsa.1829 gene 1 Human mRNA fragment for class II histocompatibility antigen beta-chain (pII-beta-4). gene2000 tumor: 1.50 2.53 2.38 3.16 3.01 2.45 1.70 2.10 3.14 2.76 1.57 4.15 3.60 5.32 2.20 1.82 2.81 5.33 4.03 2.28 1.48 2.03 1.75 1.64 2.92 1.26 1.75 2.03 2.45 2.25 2.82 3.87 1.67 1.22 2.49 1.74 4.96 1.49 1.38 5.98 mean = 2.6258 std = 1.2039 normal: 1.56 3.07 4.15 8.12 3.41 3.78 1.42 0.96 2.09 2.63 2.29 2.11 1.26 1.85 1.61 3.18 2.23 1.02 3.36 3.63 2.11 1.93 mean = 2.6261 std = 1.536

  12. histograms

  13. NORMALIZED (FREQUENCIES)

  14. T = - 0.001 P = 0.9992 t-test

  15. log2 E, center, normalize E, C&N_log2E colon date expression matrix E

  16. genes ordered by p-value 726 genes with p < 0.05 ordered by difference of means (normal – tumor)

  17. after ttest 0.05 order by diffmeans RANDOM DATA genes with p < 0.05

  18. Q=0.15 sorted p I=758

  19. 0.14 how many out of 726 are false? FDR: 726*0.14=101 false separating genes

  20. how many genes at FDR=0.05? 516*0.05=26 false separating genes

  21. 26 out of 516 - false 26 - false

  22. random data

  23. 100separating (p<0.001), 1900 random

  24. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia Armstrong et al, Nature Genetics 30, 41-47 (2002)

  25. = E1- 2E2 < 0 = E1- 2E2 > 0 E2 separation MLL E1-2E2= 0 E1 ALL

  26. w E2 projection 1 MLL E1 ALL +/- PROJECTIONS ON w – DO SEPARATE ALL FROM MLL

  27. E2 projection 2 MLL E1 ALL +/- PROJECTIONS ON w – DO NOT SEPARATE ALL FROM MLL

  28. E2 projection 3 E1 WELL SEPARATED CENTERS OF MASS - NO SEPARATION OF THE TWO CLOUDS

  29. E2 WEAK SEPARATION OF CENTERS OF MASS – GOOD SEPARATION OF THE TWO CLOUDS projection 4 E1

  30. FISHER OPTIMAL LINE TO PROJECT ON E2 Fisher to perceptron MLL PERCEPTRON E1 ALL

  31. Unsupervised analysis CLUSTERING • UNSUPERVISED ANALYSIS • GOAL A: FIND GROUPS OF GENES THAT HAVE CORRELATED EXPRESSION PROFILES. THESE GENES ARE BELIEVED TO BELONG TO THE SAME BIOLOGICAL PROCESS. • GOAL B: DIVIDE TISSUES TO GROUPS WITH SIMILAR GENE EXPRESSION PROFILES. THESE TISSUES ARE EXPECTED TO BE IN THE SAME BIOLOGICAL (CLINICAL) STATE.

  32. DEFINITION OF THE CLUSTERING PROBLEM Giraffe

  33. Dendrogram1 CLUSTER ANALYSIS YIELDS DENDROGRAM T (RESOLUTION)

  34. BUT WHAT ABOUT THE OKAPI? Giraffe + Okapi

  35. Statement of the problem2 STATEMENT OF THE PROBLEM GIVEN DATA POINTS Xi, i=1,2,...N,EMBEDDED IN D - DIMENSIONAL SPACE, IDENTIFY THE UNDERLYING STRUCTURE OF THE DATA. AIMS:PARTITION THE DATA INTO M CLUSTERS, POINTS OF SAME CLUSTER - "MORE SIMILAR“ M ALSO TO BE DETERMINED! GENERATE DENDROGRAM, IDENTIFY SIGNIFICANT, “STABLE” CLUSTERS "ILL POSED": WHAT IS "MORE SIMILAR"? RESOLUTION

  36. Dendrogram2 YOUNG OLD CLUSTER ANALYSIS YIELDS DENDROGRAM LINEAR ORDERING OF DATA T

  37. 2 4 5 3 1 1 3 2 4 5 Need to define the distance between thenew cluster and the other clusters. Single Linkage: distance between closest pair. Complete Linkage: distance between farthest pair. Average Linkage: average distance between all pairs or distance between cluster centers Agglomerative Hierarchical Clustering Distance between joined clusters The dendrogram induces a linear ordering of the data points Dendrogram

  38. Hierarchical Clustering -Summary • Results depend on distance update method • Greedy iterative process • NOT robust against noise • No inherent measure to identify stable clusters

  39. 2 good clouds COMPACT WELL SEPARATED CLOUDS – EVERYTHING WORKS

  40. 2 flat clouds 2 FLAT CLOUDS - SINGLE LINKAGE WORKS

  41. filament SINGLE LINKAGE SENSITIVE TO NOISE

  42. 2 4 5 3 1 1 3 2 4 5 Need to define the distance between thenew cluster and the other clusters. Average Linkage: average distance between all pairs Average linkage Distance between joined clusters Dendrogram

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